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The Nature of Statistical Learning Theory
, 1999
"... Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based on the deve ..."
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Cited by 13236 (32 self)
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Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based
LearningTheoretic Foundations of
, 1974
"... ABSTRACT F Some aspects of a theory of grammar are presented which derive from a formal theory of language acquisition. One aspect of the theory is a universal constraint on analyzability known as the Freezing Principle, which supplants a variety of.constraints proposed in the literature. A second a ..."
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ABSTRACT F Some aspects of a theory of grammar are presented which derive from a formal theory of language acquisition. One aspect of the theory is a universal constraint on analyzability known as the Freezing Principle, which supplants a variety of.constraints proposed in the literature. A second aspect of the theory is the Invariance Principle, a constraint on the relationship between semantic and syntactic structure that makes verifiable predictions of syntactic universals. The relationship. between the notion of "explanatory adequacy " of a theory of grammar and the learnability of a class of transformational grammars is discussed. (Author) a.0 a
Planning Algorithms
, 2004
"... This book presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning, planning ..."
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Cited by 1133 (49 self)
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, planning under uncertainty, sensorbased planning, visibility, decisiontheoretic planning, game theory, information spaces, reinforcement learning, nonlinear systems, trajectory planning, nonholonomic planning, and kinodynamic planning.
Experiments with a New Boosting Algorithm
, 1996
"... In an earlier paper, we introduced a new “boosting” algorithm called AdaBoost which, theoretically, can be used to significantly reduce the error of any learning algorithm that consistently generates classifiers whose performance is a little better than random guessing. We also introduced the relate ..."
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Cited by 2213 (20 self)
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In an earlier paper, we introduced a new “boosting” algorithm called AdaBoost which, theoretically, can be used to significantly reduce the error of any learning algorithm that consistently generates classifiers whose performance is a little better than random guessing. We also introduced
Support Vector Machine Active Learning with Applications to Text Classification
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2001
"... Support vector machines have met with significant success in numerous realworld learning tasks. However, like most machine learning algorithms, they are generally applied using a randomly selected training set classified in advance. In many settings, we also have the option of using poolbased acti ..."
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Cited by 735 (5 self)
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instances to request next. We provide a theoretical motivation for the algorithm using the notion of a version space. We present experimental results showing that employing our active learning method can significantly reduce the need for labeled training instances in both the standard inductive
Informationtheoretic metric learning
 in NIPS 2006 Workshop on Learning to Compare Examples
, 2007
"... We formulate the metric learning problem as that of minimizing the differential relative entropy between two multivariate Gaussians under constraints on the Mahalanobis distance function. Via a surprising equivalence, we show that this problem can be solved as a lowrank kernel learning problem. Spe ..."
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Cited by 359 (15 self)
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. Specifically, we minimize the Burg divergence of a lowrank kernel to an input kernel, subject to pairwise distance constraints. Our approach has several advantages over existing methods. First, we present a natural informationtheoretic formulation for the problem. Second, the algorithm utilizes the methods
K.B.: MultiInterval Discretization of ContinuousValued Attributes for Classication Learning. In:
 IJCAI.
, 1993
"... Abstract Since most realworld applications of classification learning involve continuousvalued attributes, properly addressing the discretization process is an important problem. This paper addresses the use of the entropy minimization heuristic for discretizing the range of a continuousvalued a ..."
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Cited by 832 (7 self)
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valued attribute into multiple intervals. We briefly present theoretical evidence for the appropriateness of this heuristic for use in the binary discretization algorithm used in ID3, C4, CART, and other learning algorithms. The results serve to justify extending the algorithm to derive multiple intervals. We
The strength of weak learnability
 MACHINE LEARNING
, 1990
"... This paper addresses the problem of improving the accuracy of an hypothesis output by a learning algorithm in the distributionfree (PAC) learning model. A concept class is learnable (or strongly learnable) if, given access to a Source of examples of the unknown concept, the learner with high prob ..."
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Cited by 871 (26 self)
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, the construction has some interesting theoretical consequences, including a set of general upper bounds on the complexity of any strong learning algorithm as a function of the allowed error e.
Manifold regularization: A geometric framework for learning from labeled and unlabeled examples
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2006
"... We propose a family of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal distribution. We focus on a semisupervised framework that incorporates labeled and unlabeled data in a generalpurpose learner. Some transductive graph learning al ..."
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Cited by 578 (16 self)
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We propose a family of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal distribution. We focus on a semisupervised framework that incorporates labeled and unlabeled data in a generalpurpose learner. Some transductive graph learning
Statistical Comparisons of Classifiers over Multiple Data Sets
, 2006
"... While methods for comparing two learning algorithms on a single data set have been scrutinized for quite some time already, the issue of statistical tests for comparisons of more algorithms on multiple data sets, which is even more essential to typical machine learning studies, has been all but igno ..."
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Cited by 744 (0 self)
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While methods for comparing two learning algorithms on a single data set have been scrutinized for quite some time already, the issue of statistical tests for comparisons of more algorithms on multiple data sets, which is even more essential to typical machine learning studies, has been all
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